Real Talk, Virtual Faces: A Formal Concept Analysis of Personality and Sentiment in Influencer Audiences

2026-03-25

AI Summary

This study investigates the co-occurrence patterns of sentiment, personality traits, and topical signals in audience comments directed at virtual versus human influencers, moving beyond conventional analyses that focus solely on content or statistical differences. The authors propose a two-layered, structure-priority analytical framework that, for the first time, integrates Formal Concept Analysis (FCA) into multidimensional discourse modeling. By combining iceberg support filtering, association rule mining, and Big Five personality cue detection, the framework enables a systematic examination of YouTube comments. Findings reveal that comments on human influencers exhibit a single, emotionally stable discourse structure, whereas those on virtual influencers trigger multiple discourse modes—such as appearance-oriented commentary—and display a pronounced negative sentiment bias in sensitive topics like mental health, highlighting the distinctive impact of virtual identities on audience discourse structures.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Virtual influencers~(VIs) -- digitally synthetic social-media personas -- attract audiences whose discourse appears qualitatively different from discourse around human influencers~(HIs). Existing work characterises this difference through surveys or aggregate engagement statistics, which reveal \emph{what} audiences say but not \emph{how} multiple signals co-occur. We propose a two-layer, structure-first framework grounded in Formal Concept Analysis~(FCA) and association rule mining. The first layer applies FCA with support-based iceberg filtering to weekly-aggregated comment data, extracting discourse profiles -- weekly co-occurrence bundles of sentiment, Big Five personality cues, and topic tags. The second layer mines association rules at the comment level, revealing personality--sentiment--topic dependencies invisible to frequency-table analysis. Applied to YouTube comments from three VI--HI influencer pairs, the two-layer analysis reveals a consistent structural divergence: HI discourse concentrates into a single, emotionally regulated (stability-centred) regime (low neuroticism anchoring positivity), while VI discourse supports three structurally distinct discourse modes, including an appearance-discourse cluster absent from HI despite near-equal marginal prevalence. Topic-specific analyses further show that VI contexts exhibit negative sentiment in psychologically sensitive domains (mental health, body image, artificial identity) relative to HI contexts. Our results position FCA as a principled tool for multi-signal discourse analysis and demonstrate that virtuality reshapes not just what audiences say, but the underlying grammar of how signals co-occur in their reactions.

Problem

• virtual influencers
• discourse analysis
• personality
• sentiment
• Formal Concept Analysis

Innovation

• Formal Concept Analysis
• virtual influencers
• discourse co-occurrence
• personality-sentiment-topic dependencies
• association rule mining